Machine learning architecture for risk modelling and analytics

ABSTRACT

A computer-implemented method for forecasting the pricing of at least one financial instrument, the method comprising a processor and a memory, the method comprising the operations of: generating, by the processor, a user interface on a display, said user interface comprising a user-selectable pricing tab; wherein selecting the pricing tab causes the processor to at least: receive raw data in a plurality of disparate formats; scrub the raw data for anomalies and null values using a set of rules and generate a structured data set; and wherein selecting said pricing tab causes the processor to measure best-fit correlations with respect to a company&#39;s fundamental valuation and secondary market pricing for the company&#39;s at least one financial instrument across sector peers and market conditions and generate at least one financial instrument pricing output, in real-time.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/778,926, filed on Jan. 31, 2020, which is continuation of U.S. patentapplication Ser. No. 15/488,721, filed on Apr. 17, 2017, the disclosuresof which are incorporated herein by reference.

FIELD

The present disclosure relates to computer-implemented methods forsupporting multiple functions such as communication, informationmanagement, deal execution, stakeholder collaboration, pricingcalculation, securities offering and issuance, and analytics forissuers, investors, and dealers.

BACKGROUND

The primary market and the process of origination for securities has notchanged for decades and is still a manual process that is paper heavyand includes phone calls and excel spreadsheets. For example, bondorigination and over-the-counter (OTC) trading are in dire need for animprovement. The current fixed income capital market data flows areinefficient in many respects, limiting precision in assigning value tocredit risk long term. Markets remain heavily reliant on segregated andmanual data operations between counterparties and consequently,disparate data sets. These disparate data sets cause the market tosuffer from information asymmetry and decentralization. As a result,insight from available data is fragmented and disseminated throughmanual exchanges between counterparties, which furthers creation ofdisparate data sets.

The existing process lacks transparency, is time consuming, and impedesthe efficient allocation of capital. Since the 2008 financial crisis,increasingly stringent regulation has adversely impacted dealer'smarket-making capabilities in bond markets. When coupled with increasingnew issuances driven by the low interest rate environment, there hasbeen a sharp decline in secondary market trading activities, which inturn has exacerbated primary market challenges including inefficient newissue pricing and price discovery. In addition, the manual nature of theexisting process makes the primary market inaccessible to many investorsincluding some institutional and many retail investors. This isundesirable for a well-functioning capital market. Furthermore, becauseof these structural problems, many corporate issuers have a limitedability to raise capital in the institutional capital markets as theyneed to meet high size and scale requirements to justify costs andoperational inefficiencies involved in the process.

Additionally, issuers, investors, and dealers exchange many disparatepieces of information and market analysis all in different formats andare each then consumed by cumbersome manual reviews. Market informationtends to be point-in-time and is not useful in a market that changesevery day.

Transparency during the sales process is also lacking, this includestransparency in pricing, costs, allocations, and supply and demand ingeneral. Additionally, issuers do not have tools to prepare for newissue offerings or to manage relationships with their dealers andinvestor base. Vitally, dealers, issuers and investors do not have toolsto gauge market interest between one another regarding potentialofferings. All market participants also have limited ability to trackactivities involved in securities offerings for the purpose ofregulatory and internal management reporting.

Transaction logistics in the primary market are phone call and emailbased. Time and financial resources lost to sending and receivingdocuments, aggregating and processing market information, regulatorycompliance, performing manual credit research searching electronicmailboxes, making phone calls, and trying to contain information leakageis staggering. In addition, financial analysis is not real-time and doesnot help market participants make data driven decisions.

It is an object of the present disclosure to mitigate or obviate atleast one of the above-mentioned disadvantages.

SUMMARY

In one of its aspects, a computer-implemented method for forecasting thepricing and timing of issuing at least one financial instrument, themethod comprising a processor and a memory, the method comprising theoperations of:

generating, by the processor, a user interface on a display, said userinterface comprising a plurality of user-selectable tabs comprising: apricing tab, an issuance tab, and a matching tab;

wherein selecting said pricing tab causes the processor to measurebest-fit correlations with respect to a company's fundamental valuationand secondary market pricing for the company's at least one financialinstrument across sector peers and market conditions and generate atleast one financial instrument pricing output, in real-time;

wherein selecting said issuance tab causes the processor to performmeasurement of at least one financial instrument issuer's propensity toissue the at least one financial instrument, and to assign a propensityscore which estimates the relative likelihood the issuer will issue theat least one financial instrument within a time frame; and output anissuance recommendation for the issuer; and

wherein selecting said matching tab causes the processor to employalgorithmic matching of target buyers with the at least one financialinstrument, based on at least one of past buying patterns, portfoliomanager preferences, rebalancing events and preferred industry sector,rating or tenor; and to generate a ranking score indicative of thetarget buyer's likelihood to purchase the at least one financialinstrument.

In another of its aspects, a computer-implemented method for trading inprimary or secondary market offerings of securities, the methodcomprising a processor and a memory, the method comprising theoperations of:

monitoring, by the processor, current and historical secondary markettrading levels of correlated securities;

receiving, by the processor, raw data comprising new issue pricinglevels and secondary traded pricing levels from a plurality of dealersand capital markets data sources, wherein the raw data is in a pluralityof disparate formats;

converting, by the processor, the plurality of disparate formats into astandardized format;

responsive to said monitoring, by the processor, predicting in real-timeat least one of issue likelihood, new issue pricing levels and secondarytraded pricing levels of a plurality of issuers of the securities andthe new issue pricing levels and secondary traded pricing levels of thesecurities;

converting, by the processor, the new issue pricing levels and secondarytraded pricing levels to equivalent levels in any one of a plurality offoreign currencies and any one of a plurality of interest rates;

matching, by the processor, at least one target buyer to specificissuers based on the at least one of the predicted issue likelihood andthe predicted new issue pricing levels and the predicted secondarytraded pricing levels.

In another of its aspects, a computer-implemented method for forecastingthe pricing of at least one financial instrument, the method comprisinga processor and a memory, the method comprising the operations of:

generating, by the processor, a user interface on a display, said userinterface comprising a user-selectable pricing tab; wherein selectingthe pricing tab causes the processor to at least:

receive raw data in a plurality of disparate formats;

scrub the raw data for anomalies and null values using a set of rulesand generate a structured data set; and

wherein selecting said pricing tab causes the processor to measurebest-fit correlations with respect to a company's fundamental valuationand secondary market pricing for the company's at least one financialinstrument across sector peers and market conditions and generate atleast one financial instrument pricing output, in real-time.

In another of its aspects, a computer-implemented method for forecastingthe issuance of at least one financial instrument, the method comprisinga processor and a memory, the method comprising the operations of:

generating, by the processor, a user interface on a display, said userinterface comprising a user-selectable issuance tab;

wherein selecting the issuance tab causes the processor to at least:

receive raw data in a plurality of disparate formats;

scrub the raw data for anomalies and null values using a set of rulesand generate a structured data set; and

wherein selecting said issuance tab causes the processor to performmeasurement of at least one financial instrument issuer's propensity toissue the at least one financial instrument, and to assign a propensityscore which estimates the relative likelihood the issuer will issue theat least one financial instrument within a time frame; and output anissuance recommendation for the issuer.

In another of its aspects, a computer-implemented method for trading insecurities, the method comprising a processor and a memory, the methodcomprising the operations of:

monitoring, by the processor, current and historical secondary markettrading levels of correlated securities;

receiving, by the processor, raw data comprising new issue pricinglevels and secondary traded pricing levels from a plurality of dealersand capital markets data sources, wherein the raw data is in a pluralityof disparate formats;

converting, by the processor, the plurality of disparate formats into astandardized format;

responsive to said monitoring, by the processor, predicting in real-timeat least one of issue likelihood, new issue pricing levels and secondarytraded pricing levels of a plurality of issuers of the securities andthe new issue pricing levels and secondary traded pricing levels of thesecurities;

converting, by the processor, the new issue pricing levels and secondarytraded pricing levels to equivalent levels in any one of a plurality offoreign currencies and any one of a plurality of interest rates.

In another of its aspects, a computer readable medium storinginstructions executable by a processor to carry out the operationscomprising:

aggregating raw data from a plurality of data sources comprisingcontemporaneous trading data and fundamental data covering a series oftime periods and one or more aspects of quantitative investing andmarket monitoring, said raw data in a plurality of disparate formats;

transforming said raw data in the plurality of disparate formats into asingle standard format to generate structured data;

extracting at least one data element of value associated with at leastone financial instrument from the structured data in accordance with oneor more pre-programmed functions;

establishing a plurality of input nodes and an output node for arecurrent neural network model for each aspect of quantitative investingand market monitoring;

using the recurrent neural network model to build at least one model;

inputting the structured data into the recurrent neural network modelusing the plurality of input nodes;

training each of the recurrent neural network using said inputs until anerror function associated with an output value that corresponds to anaspect of quantitative investing and market monitoring is minimized; and

using one or more weights from the trained recurrent neural networkmodels to identify a set structured data by element of value and outputthat will be used as an element of value summary for use as an input toeach of one or more predictive models;

normalizing each of the one or more sets of structured data by dataelement of value, refining the sets of structured data by the dataelement of value,

creating a summary of a refined transaction data set for each dataelement of value, and

using the data element of value summaries as inputs to a predictivemodel for each of the one or more aspects of quantitative investing andmarket monitoring where the aspects of quantitative investing and marketmonitoring comprising managing trading activities, managing risk, makingportfolio funding allocations, predicting a time horizon for issuance ofthe at least one financial instrument; predicting an issuer of the atleast one financial instrument within the predicted time horizon;predicting a price of the at least one financial instrument, matching abuyer with the at least one financial instrument, and combinationsthereof, and

wherein the predictive models are useful for completing tasks comprisingmanaging trading activities, managing risk, making portfolio fundingallocations, predicting a time horizon for issuance of the at least onefinancial instrument; predicting an issuer of the at least one financialinstrument within the predicted time horizon; predicting a price of theat least one financial instrument, matching a buyer with the at leastone financial instrument, and combinations thereof.

In another of its aspects, there is provided an interactive digitalplatform for trading in primary market offerings of securitiescomprising a pre-deal activity module and a deal execution module. Thepre-deal activity module allows a plurality of users to perform creditand market analysis, predictive analytics, communications functions,relationship management, and information management. The deal executionmodule allows the plurality of users to perform a deal executionworkflow, order management, best execution analysis, documentationmanagement, and regulatory compliance.

In another of its aspects, the credit and market analysis comprisespublishing pricing levels to other users of the plurality of users usinga common format, swap analysis, evaluation of secondary marketliquidity, machine comparison of covenant terms, and evaluation ofprofiles of the plurality of users.

In another of its aspects, the predictive analytics comprises machinelearning and big data, evaluating participation in primary markets, theevaluation of current and historic secondary market trading levels ofcorrelated securities, and the prediction of new issue levels.

In another of its aspects, the communications functions comprisepublishing pricing indications publicly or privately to other users ofthe plurality of users.

In another of its aspects, the relationship management comprises thetracking of historical records of deal participation by the plurality ofusers.

In another of its aspects, the information management comprisesreceiving a plurality of digitized primary market data from a pluralityof sources, the platform digitizing the plurality of digitized primarymarket data, converting the plurality of digitized primary market datainto a common format, and storing the plurality of primary market datainto a database.

In another of its aspects, the deal execution workflow comprisesenabling the plurality of users to create a plurality of deals andpopulate the plurality of deals with a plurality of existing reverseinquiries, soft sounding being used to gauge interest in the pluralityof deals.

In another of its aspects, the order management comprises enabling theplurality of users to populate and allocate orders and submit orders fortrading.

In another of its aspects, the best execution analysis comprises theutilization, by the platform, of bid and offer data to produce aweighting of pricing trends.

In another of its aspects, the documentation management comprises theindexing of the plurality of digitized primary market data to enable theplurality of users to search the plurality of digitized primary marketdata.

Advantageously, the present disclosure mitigates limitations within theprior art relating to the field of computer-assisted business methods,and to systems for implementing such methods, and more specifically, tocomputer-based methods for supporting multiple functions such ascommunication, information management, deal execution, stakeholdercollaboration, pricing calculation, securities offering and issuance,and analytics for issuers, investors, and dealers.

In addition, there is a great need for a fixed income big-datacentralization where advanced analytics such as price discovery,liquidity risk management, intelligence gathering, pre-trade andpost-trade analytics can be performed globally, to increase the overallefficiency of the fixed income market and understanding of the creditrisk valuations. With no centralized hub, issuers and investors operatewith partial awareness. Accordingly, the present disclosure provides acentralized big-data hub powered with artificial intelligence (AI)capabilities for fixed income analytics. The centralized big-data hubcomprises an AI application utilizing deep historical data records offundamental data elements (audited statements, dealer supplied primaryand secondary bond price quotations etc.) and secondary market bondtransactions, and can solve this problem.

BRIEF DESCRIPTION OF THE DRAWINGS

Several exemplary embodiments of the present disclosure will now bedescribed, by way of example only, with reference to the appendeddrawings in which:

FIG. 1A shows an operating environment for a computerized end-to-endplatform for primary bond origination in a fixed income market;

FIG. 1B shows an overview of the platform and its workflow that allowsusers to perform all primary market-related activities on the platform;

FIG. 1C shows select data sets that are pre-processed for priceanalytics engine, predictive issuance analytics engine, and discoveryand matching engine;

FIG. 2A shows exemplary user interface with a dashboard associated withpricing, issuance and discovery of financial instruments, such as bonds;

FIG. 2B illustrates a price analytics engine for minimizing creditpricing risk and enabling systematic monitoring of credit pricing in aplurality of currencies and monitoring the cost of swapping proceedsfrom a first currency to a second currency;

FIG. 2C shows exemplary steps taken by price analytics engine 34 formonitoring and performing advanced analysis 100 on pricing, credit, andmarket data.

FIG. 2D illustrates a model of the price analytics engine for measuringbest-fit correlations with respect to company fundamental valuation andsecondary market pricing for their bonds across sector peers and marketconditions;

FIG. 2E shows tenor-yield curves for various companies;

FIG. 2F shows a historical spread for one International SecuritiesIdentification Number (ISIN);

FIG. 2G shows a historical spread for another ISIN;

FIG. 2H shows an actual spread and predicted spread (in-time prediction)for the ISIN of FIG. 2F based on model implemented with a BidirectionalLong Short-term Memory (BLSTM) neural network;

FIG. 2I shows an actual spread and predicted spread (out-of-timeprediction) for the ISIN of FIG. 2F based on model implemented with aBidirectional Long Short-term Memory (BLSTM) neural network;

FIG. 2J shows an actual spread and predicted spread (in-time prediction)for the ISIN of FIG. 2G based on model implemented with a BidirectionalLong Short-term Memory (BLSTM) neural network;

FIG. 2K shows an actual spread and predicted spread (out-of-timeprediction) for the ISIN of FIG. 2G based on model implemented with aBidirectional Long Short-term Memory (BLSTM) neural network;

FIG. 2L shows a flow chart outlining exemplary steps implemented by aprice analytics engine;

FIG. 2M shows a user interface with a modeled yield curve for aparticular issuer;

FIG. 3A illustrates a suite of predictive analytics tools that provideusers with unique market insights;

FIG. 3B outlines the exemplary steps of the issuance engine executed bya processor for predicting if an issuer will come to the market andissue bonds;

FIG. 3C shows an example of an output issuance recommendation for oneissuer, exposing underlying feature importance;

FIG. 3D shows a sample dashboard for tracking multiple issuanceopportunities on the platform and a sample of the table nomenclatureexposed via an API for continuous data download;

FIG. 3E shows a sample back-tested performance of the algorithms of theissuance engine, plotting predictions for a specific issuer to issuebonds in a specific tenor, in one example.

FIG. 3F shows the overall result predictions categorized in 4 buckets:Highly Likely to Issue, Likely to Issue, Unlikely to issue, and Veryunlikely to Issue;

FIG. 3G shows an output graph for the historical propensity for a singleissuer and single tenor prediction, with the time across the x-axis, andthe propensity score on the y-axis labeled as ‘Likelihood To Issue’;

FIG. 3H illustrates a discovery and matching engine for matching atleast one target institutional buyer with the fixed income marketopportunity;

FIG. 3I outlines the exemplary steps of the discovery and matchingengine executed by a processor for matching at least one targetinstitutional buyer with the fixed income market opportunity;

FIG. 3J shows a user interface with bond buyer matching results;

FIG. 4 illustrates a suite of communication tools that provide userswith multiple, user-friendly, channels of communications;

FIG. 5 shows an overview of the suite of relationship management tools;

FIG. 6 shows an overview of systems to aggregate, process, and integratedata from multiple sources into and out of a centralized dataaggregation and processing, in the context of primary capital markets;

FIG. 7 shows an overview of customizable digital deal execution workflowwith permission-controlled access provided to multiple stakeholders;

FIG. 8 shows an overview of digital order book management and allocationsystems;

FIG. 9 shows an overview of customizable digital deal execution workflowwith permission-controlled access provided to multiple stakeholders;

FIG. 10 shows a suite of tools available for users to manage multiplefinancial documents;

FIG. 11 shows a suite of tools available for users to comply with therelevant regulatory compliance requirements;

FIG. 12 shows an overview of continuous supervised machine learningprocess using regression models for the purpose of Digital New IssueIndication predictive analytics tool;

FIG. 13 shows an overview of continuous supervised machine learningprocess using classification models for the purpose of Lead DealerPrediction predictive analytics tool; and

FIG. 14 illustrated a table of financial data and rates.

DETAILED DESCRIPTION

The detailed description of exemplary embodiments of the inventionherein makes reference to the accompanying block diagrams and schematicdiagrams, which show the exemplary embodiment by way of illustration.While these exemplary embodiments are described in sufficient detail toenable those skilled in the art to practice the invention, it should beunderstood that other embodiments may be realized and that logical andmechanical changes may be made without departing from the spirit andscope of the invention. Thus, the detailed description herein ispresented for purposes of illustration only and not of limitation. Forexample, the steps recited in any of the method or process descriptionsmay be executed in any order and are not limited to the order presented.

Moreover, it should be appreciated that the particular implementationsshown and described herein are illustrative of the invention and itsbest mode and are not intended to otherwise limit the scope of thepresent disclosure in any way. Connecting lines shown in the variousfigures contained herein are intended to represent exemplary functionalrelationships and/or physical couplings between the various elements. Itshould be noted that many alternative or additional functionalrelationships or physical connections may be present in a practicalsystem.

Embodiments of the invention comprise a system that will digitizeprimary market data, including the processes, logistics, analytics,issuances, communication, collaboration, information management,relationship management, predictive analytics, cognitive computing andbig data analytics, and any other functions. Users for this platform areissuers, dealers, investors, and/or any other primary marketparticipants, and the platform digitizes their experience withdeal-related and non-deal-related primary market activities.

The platform brings all primary market participants, including issuers,dealers, investors, legal counsel, and rating agencies, among others, onto a digital platform. The platform automates manual functions,increases market transparency, facilitates price discovery, allows usersto execute primary market deals, and aggregates, processes and managesinformation, among others.

Embodiments of the invention comprise an electronic system built for thecapital markets that allows users to perform a plurality of thefollowing functions: manage information, documentation, relationships,and logistics; communicate directly with issuers, dealers, and/orinvestors, among others; submit inquiries and/or bids directly to anissuer; manage the deal lifecycle; distribute securities usingconventional clearing and settlement methods; generate and fill an orderbook; allocate orders; leverage tools to distribute data betweenissuers, dealers, and investors, among others; conduct auctions formultiple securities from one or more issuers; utilize interactivecalendaring functions, meeting management, marketing campaigns,roadshows, among other marketing and sales activities; access real-timemarket analytics and indices covering fixed income and/or equitymarkets; generate cognitive computing and big data analysis from theplatform directly; use predictive analytics; and generate custom reportsfor any of the features or views based on the underlying subject matter.

The electronic system is designed for parties involved in the capitalraising industry. The platform comprises a secure cloud-based platformthat employs both systemized and ongoing user verification andidentification protocols. The platform's cloud infrastructure ensureshighest availability and performance with multiple availability zonesand data centres globally. The platform allows users to communicate andbuild relationships with other issuers, dealers and investors; send andreceive financial information efficiently; manage all primarymarket-related information in one-place; and perform advanced andpredictive analytics using private and public data. Through thesecapabilities, the platform provides tools to assist issuers in allstages of capital raising and further comprises using data-drivenmethods to enhance investor and dealer relationship management,communicating with key stakeholders real-time on a secure system,discreetly discovering potential investor demand, and accessing the mostup-to-date market intelligence directly from dealers, investors andother participants. Similarly, the platform provides sophisticated toolsto assist investors with all stages of investing in new issues ofsecurities including building and measuring relationships with differentmarket stakeholders, communicating with key stakeholders real-time on asecure system, enhancing decision-making with sophisticated creditanalysis tools and intelligence, digitally discovery price and new issuesupply through a discreet channel, and improving operational efficiencythrough the use of a centralized depository for all relevant documents.

FIG. 1A illustrates an overview of a computerized end-to-end platform 10for primary bond origination in a fixed income market, in which theend-to-end platform and support framework for receiving and processingfinancial market data. Platform facilitates pre-deal data processing andmulti-party communication, deal execution transition, and deal executionworkflow. The workflow is highly customizable. Depending on deal typeand asset class, the platform's deal execution workflow is customizablein terms of execution stages, fields, and conditions. The platformprovides a seamless suite of modules that enable users to participate inthe primary capital markets, in the context of both pre-deal dataprocessing and communication or deal related market activities. Theplatform provides access to three core user groups; securities issuers,investors, and dealers and comprises an end-to-end primary marketplatform. Given the heavily regulated nature of the debt issuanceindustry, the platform provides deal workflow depending on marketsegment and region, provides recordkeeping and an audit trail, andallows for strict information control.

Computerized end-to-end platform 10 provides a centralized hub whereadvanced analytics such as price discovery, liquidity risk management,intelligence gathering, pre-trade and post-trade analytics can beperformed globally, thereby increasing the overall efficiency of thefixed income market and understanding of the credit risk valuations forissuers and investors. Platform 10 uses deep historical data records offundamental data elements (audited statements, dealer supplied primaryand secondary bond price quotations etc.) and secondary market bondtransactions to provide fixed income analytics.

Platform 10 comprises computing means with computing system 12comprising at least one processor such as processor 14, at least onememory device such as memory 16, input/output (I/O) module 18 andcommunications interface 20, which are in communication with each othervia centralized circuit system 22, as shown in FIG. 1A. Althoughcomputing system 12 is depicted to include only one processor 14,computing system 12 may include a number of processors therein. In anembodiment, memory 16 is capable of storing machine executableinstructions, data models and process models. Database 23 is coupled tocomputing system 12 and stores pre-processed data, model output data andaudit data. Further, the processor 14 is capable of executing theinstructions in memory 16 to implement aspects of processes describedherein. For example, processor 14 may be embodied as an executor ofsoftware instructions, wherein the software instructions mayspecifically configure processor 14 to perform algorithms and/oroperations described herein when the software instructions are executed.Alternatively, processor 14 may be execute hard-coded functionality.Computerized end-to-end platform 10 may be software (e.g., code segmentscompiled into machine code), hardware, embedded firmware, or acombination of software and hardware, according to various embodiments.

Examples of the I/O module 18 include, but are not limited to, an inputinterface and/or an output interface. Some examples of the inputinterface may include, but are not limited to, a keyboard, a mouse, ajoystick, a keypad, a touch screen, soft keys, a microphone, and thelike. Some examples of the output interface may include, but are notlimited to, a microphone, a speaker, a ringer, a vibrator, a lightemitting diode display, a thin-film transistor (TFT) display, a liquidcrystal display, an active-matrix organic light-emitting diode (AMOLED)display, and the like. In an example embodiment, processor 14 mayinclude I/O circuitry for controlling at least some functions of one ormore elements of I/O module 18, such as, for example, a speaker, amicrophone, a display, and/or the like. Processor 14 and/or the I/Ocircuitry may control one or more functions of the one or more elementsof I/O module 18 through computer program instructions, for example,software and/or firmware, stored on a memory, for example, the memory16, and/or the like, accessible to the processor 14.

Communications interface 20 enables computing system 12 to communicatewith other entities over various types of wired, wireless orcombinations of wired and wireless networks, such as for example, theInternet. In at least one example embodiment, communications interface20 includes a transceiver circuitry for enabling transmission andreception of data signals over the various types of communicationnetworks. In some embodiments, communications interface 20 may includeappropriate data compression and encoding mechanisms for securelytransmitting and receiving data over the communication networks.Communications interface 20 facilitates communication between computingsystem 12 and I/O peripherals.

Centralized circuit system 22 may be various devices for providing orenabling communication between the components (12-20) of computingsystem 12. In certain embodiments, centralized circuit system 22 may bea central printed circuit board (PCB) such as a motherboard, a mainboard, a system board, or a logic board. Centralized circuit system 22may also, or alternatively, include other printed circuit assemblies(PCAs), communication channel media or bus.

A plurality of user computing devices 24 and data sources 26 are coupledto computing system 12 with communication network 28. User computingdevices 24 can therefore access platform 10 to run queries and receiverequested market insights and predictions based on financial market datafrom data sources 26. Platform 10 can be operable to register andauthenticate users (using a login, unique identifier, and password forexample) prior to providing access to applications, a local network,network resources, other networks and network security devices.

Looking at FIG. 1B, processor 14 can execute instructions in memory 16to configure pre-deal utility 30 and deal execution utility 32, andother functions described herein. As shown in FIG. 1B, pre-deal activitymodule 30 allows a plurality of users to perform credit and marketanalysis 100, predictive analytics 101, communications functions 102,relationship management 103, and information management 104. Dealexecution module 32 allows the plurality of users to perform acustomized deal execution workflow 110, order management and order bookallocation methodologies 111, best execution analysis 112, documentationmanagement 113, and regulatory compliance 114.

In more detail, pre-deal utility 30 comprises a suite of predictivealgorithms for the fixed income capital markets, such as price analyticsengine 34, predictive issuance analytics engine 38, and discovery andmatching engine 38, which receive pre-processed data derived from aplurality of raw data sources 26. Processor 14 is configured by themachine executable instructions to receive input data for processing bythe pre-deal utility 30 and deal execution utility 32 using the datamodels to generate pricing and issuance predictions associated withfinancial instruments, and matching recommendations of financialinstruments to buyers and issuers. Exemplary financial instruments mayinclude currency; debt; bonds, loans; equity shares; derivatives;options, futures, forwards.

As shown in FIG. 1C, select data sets are pre-processed by processor 14.Exemplary data families received and pre-processed for price analyticsengine 34, predictive issuance analytics engine 38, and discovery andmatching engine 38 comprise:

secondary market spread movements, sourced from Thompson Reuters™ withan Interday update frequency, and comprises the intraday transactionprices of companies' bonds are used to measure spread movements and thecurrent cost of funding for all companies in the coverage universe;

recent issuance pricing levels and dealer quotations, sourced fromThompson Reuters and Proprietary Network, with an Interday updatefrequency. At issuance securities pricing levels allows for comparisonof at issuance pricing versus first 5 days of trading. Primary dealerquotation averages allow for model calibration with respect topre-issuance quotations and supply-demand metrics versus at issuance andpost issuance price performance;

company credit ratings, sourced from S&P™, Moody's™, DBRS (Canada)™,Fitch (USA)™, updated on a weekly basis, or quarterly. Issuer's pastbond issuances and their ratings as well as composite rating for theissuer overall indicate the company's risk level and benchmarkingcategory. This data is used to train the models and to back-test theaccuracy of price analytics engine 34 output;

company fundamental data, sourced from S&P Global Market IntelligenceWeekly updates, updated on a weekly basis, or quarterly. The company'sfundamental financial data is an indicator of the company'scredit-worthiness, and by extension, their cost of borrowing acrosstenors. In addition, fundamental metrics indicate the liquidity need ofthe company and its short term need to raise financing. The financialprofile of a company aids with clustering analysis of companies withsimilar characteristics. It is expected that companies with similarfinancial characteristics and balance sheets would have similar bondissuance patterns;

eMAXX Investor Holdings, sourced from Data eMAXX Investor Holdings Data,updated on a quarterly basis. Thomson Reuters™ providessecurity-specific data on corporate, government, municipal, and MBS bondholdings for more than 2,900 investor portfolios including their coupontype, maturity, credit rating, and par value;

investor insights campaigns, proprietary data sourced from OverbondLimited, updated on a monthly basis. A Community of more than 250institutional investors provides indicative sector, tenor, price andsize preferences for hypothetical issuance in investment grade and highyield credit. For example, discovery and matching engine appliesaggregate investor preference to calibrate traditional andnon-traditional buyer patterns;

prospectus filings, sourced from EDGAR (USA)™, SEDAR (Canada)™, publicfilings international Daily/when filed Prospectus filings is anindicator that a company deterministically plans to raise additionalfinancing;

macro market data, sourced from Central Banks/Treasuries and publicsources, with an Interday update frequency. Changes in interest ratesand economic data has an impact on the attractiveness of the fixedincome markets and the availability of credit, and by extension,likelihood for companies to issue bonds;

outstanding securities, sourced from Thomson Reuters, with an Interdayupdate frequency. The outstanding securities allows for calculation ifthe company has upcoming maturities that need to be refinanced. Thematurity schedule of the outstanding securities is used to calculategaps which may increase issuer likelihood to issue in a specific tenor;

historical bond issuance, sourced from Thomson Reuters, with an Interdayupdate frequency. Issuer's past bond issuances indicate issuancefrequency, seasonality, and propensity for specific tenors, and may beused to train the models and to back-test the accuracy of predictiveissuance analytics engine 38's predictions; and

industry sector information, sourced from Thomson Reuters, PublicSources, systematically updated. Different industry sectors have vastlydifferent bond issuance patterns and frequencies. The models are tunedto each sector specificity and issuers are grouped to their closestpeers.

FIG. 2A shows exemplary user interface 120 with a dashboard associatedwith pricing, issuance and discovery of financial instruments, such asbonds. User interface 120 comprises header section 121, selected issuersection 122, and recent deal section 123. Header section includes aplurality of tabs, such as dashboard tab 124 a, relationships tab 124 b,quotes tab 124 c, demand discovery tab 124 d, and deal room tab 124 e.Search field 125 allows users to enter queries pertaining to issuers,dealers, and so forth. Icons 126 also appear in section 121, and pertainto user account, platform tools, notifications and administrator portal.

Selected issuer section 122 comprises identification 132 of the selectedissuer, relevant news summaries 134, weekly new issue volume 140 andassociated drop-down menu to select bond type 142, weekly average spreadvolume 144 and associated drop-down menu to select bond type 146. Recentdeal section 123 includes drop-down menu 147 for selecting bond type, alist of issuers 148 a, associated sector 148 b for each respectiveissuer 148 a, currency 148 c, amount 148 d, issue yield 148 e, coupon148 f, issue date 148 g and maturity date 148 h.

In more detail, FIG. 2B illustrates price analytics engine 34 forminimizing credit pricing risk and enabling systematic monitoring ofcredit pricing in a plurality of currencies and monitoring the cost ofswapping proceeds from a first currency to a second currency. Priceanalytics in different liquidity buckets and integrated machine-learningmodules provide a reduction in credit pricing risk, enabling systematicmonitoring of credit pricing in a variety of currencies e.g. all G10currencies, covering large universe of issuer names as well asmonitoring of the cost of swapping proceeds from foreign currency todomestic currency.

Price analytics engine 34 comprises a suite of digital tools that allowusers to monitor and perform advanced analysis 100 on pricing, credit,and market data. Digital New Issue Level Indication module 200 provideusers with an ability to publish indicative new issue pricing levels(“Pricing Indication”) to other market participants. Currently, issuersand investors receive these indications from multiple dealers on aweekly basis in disparate formats and channels. The purpose of suchcommunication is to allows users to indicate their view of the pricinglevel of a new issue for a specific issuer given the prevailing marketconditions. Through digitizing the process and converting data intostructured forms, platform 10 is able to generate intelligence byutilizing machine learning and big data technologies to provide advancedpredictive analytics and data-driven insights. FIG. 14 illustrates anexample of a Pricing Indication sheet sent by a dealer to an issuer.

The Digital New Issue Indication 200 tool allows dealers to manage andcommunicate these indications in one place through platform 10. Throughthe use of platform 10, dealers are able to publish pricing indicationspublicly or privately with specified target user groups. Moreover, userscan communicate with their internal team members to collaborate onpreparing indicative new issue levels prior to publishing andcommunicating with their clients. Further, users are able to generateand send indicative pricing sheets in multiple formats such as PDF andMicrosoft Word through other delivery channels including email. Usersreceiving the Pricing Indications can aggregate all Pricing Indicationsreceived on platform 10 or view them through traditional communicationchannels such as a PDF attachment in an email. Additionally, users areable to communicate the current secondary levels, commentaries on themarket, and peer group indicative new issue levels through platform 10.Such information is often used to support the Pricing Indications quotedby market participants. Platform 10 allows for advanced visualization ofthe aggregated data through the Secondary Level Analysis andVisualization 201, Historical Trend Analysis 202, Historical DealAnalysis 203, and Sector & Peer Comparison 204 tools. The aggregateddata will be executed by algorithm to form unbiased analysis. Unliketraditional methods that may require longer time to collect data andanalyze, platform 10 automates the process and deliver new insights thatare currently unavailable. Users are able to aggregate data received onplatform 10 and data transmitted through emails and APIs.

Credit and Market Analysis 100 contains several other tools to allowusers to analyze the primary market. Namely, the Swap Calculator 205allows users to convert new issue pricing levels from the platform toequivalent levels in foreign currencies and different interest ratestructures. For example, an issuer is able to use the Swap Calculator205 to find the swapped equivalent rate of its USD and EUR new issues toassess the attractiveness of issuing debt in either USD or EUR. TheSecondary Market Liquidity Gauge 206 integrates public data, forexample, TRACE, with the user's proprietary private data to gaugecurrent secondary market liquidity, given a particular set of securitiesand particular group of dealers. The aggregate data is used to teachmachine learning algorithms for the purpose of generating new insightsthat are not currently available. Such a tool is used by primary marketparticipants given the deteriorating secondary market liquidityconditions, driven in part by new regulations. Additionally, theCovenant Analysis Tool 207 allows users to view the covenant set offeredby a particular security issuer, compare it over past new issues, andanalyze each covenant in detail by accessing the specific covenantslanguage contained in offering documents. Platform 10 is designed torecognize the similarities or certain patterns in covenant language. TheBond Price Calculator 208 allows users to determine the price of newissue levels and secondary issue levels. The Documentation Lookup andAnalysis 209 tool allows users to access relevant financial documentsrelated to each issuer. Platform 10 uses a document conversion tool toconvert all documents in multiple formats into a standardized format toallow for advanced indexing suited for big data analytics and searching.Platform 10 relies on file formats and conversion methods includingnative application and special conversion action to process fileconversions. Further, platform 10 implements search engine techniques toenable users to search relevant key words efficiently. Platform 10 alsohosts comprehensive Dealer, Issuer, and Investor Profiles 210 to allowusers to quickly identify each other and perform analysis.

Now referring to FIG. 2C, there is shown details of the price analyticsengine 34. As described above, platform 10 sources raw trading andfundamental data via automated scripts executed at predeterminedintervals e.g. every 24 hours (step 2000). As noted above, raw data issourced from major data suppliers in the financial sector, includingThompson Reuters (secondary bond issuance and trading levels), S&PGlobal Market Intelligence (company level fundamental data), Ratingagency composite (company ratings and macro market data), as well asvarious other sources. Platform 10 sources proprietary data, aggregatedand anonymized dealer quotations from a community of large IG issuers,and investor preferences through direct feedback loops.

In step 2002, this raw data is pre-processed and the trading data andfundamental data is structured and mapped to the appropriate issuer ID,and stored in databases 27 (step 2004). The data is systematicallyscrubbed for anomalies and null values. Finally, a set of key inputfactors are generated based on the raw input (step 2006). These includebut are not limited to factors that measure secondary market spreadmovements, recent issuance pricing levels, nearest neighbor creditratings and fundamental financial metrics. These factors are dividedbetween sector and company specific and are used as inputs to themachine learning models.

The structured data is fed into the machine learning algorithm astraining data to generate several models to calculate the output pricinglevels (step 2008). Exemplary machine learning algorithm comprisesthree-phases engineered to measure best-fit correlations with respect tocompany fundamental valuation and secondary market pricing for theirbonds across sector peers and market conditions at large, with modelstuned for different liquidity scenarios. As an example, these models areeach trained using a subset of the past data, ranging from one day, onemonth to a maximum of ten years, for example. Advanced samplingtechniques are used to account for data gaps for illiquid issuers inorder to construct yield curves for all tenors and all issuers incoverage universe. An issue pricing level for a new is output in step2010.

FIG. 2D a flow chart outlining exemplary steps implemented by priceanalytics engine 34, in one exemplary implementation, and withadditional reference to FIGS. 1a, 1b , 2, 4 a-10. In step 3000,real-time trading data is fed into a dataextraction/transformation/loading (ETL) data pipeline for processing,and historical data is also retrieved from a plurality data sources(step 3002) and fed into ETL data pipeline for processing (step 3004).Accordingly, data is retrieved from data from one or more sources, e.g.streaming data, in real-time or near real-time (extraction); and thedata is reformatted (transformation); and the data is loaded into targetdatabase 27. The stored data may include historical quotes (step 3006)and the latest quotes updated in real-time e.g. a table of the latest 8hours of trade activities (step 3008).

In step 3010, the stored data in step 3008 is cleansed and filteredusing predefined rules and conditions. A liquidity check is performedfor each company to identify illiquid securities (step 3012), and suchchecks may be performed at predetermined time intervals or at particulartimes of day. The identified illiquid securities are stored in adatabase (step 3014).

In step 3016, a training data set is built and a training model forbenchmark is generated and stored for use in the next steps. A finaltable is updated in real-time for predictions (step 3018). From step3016, several machine learning (ML) models (SVR), e.g. 60-70 ML models,are trained on predefined high activity companies (high issuers) dataand a prediction for a plurality of securities e.g. 1,200 or more, ismade (step 3020) and the models are saved and the final table is updatedin real-time for predictions (step 3018). Next, data for other issuerse.g. 100 or more is collected and models from the previous steps areused to find Nearest Neighbours (step 3022), this step is performed atpredefined intervals or at specific times during the day. The outcome ofstep 3022 is stored in a Nearest Neighbour table (step 3024). Next, datais collected for other issuers e.g. more than 100 and models fromprevious steps and their training data and Nearest Neighbour data fromNearest Neighbour table is used to train models for other issuers andpredict the securities pricing, and the model is stored (step 3026).

In another implementation, price analytics engine 34 is able to handlethe illiquid nature of the primary and secondary market. Generally, AIalgorithms require large amount of data to internalize market charactersto produce accurate results. Due to the illiquid nature of the fixedincome market, secondary market data may have some gaps. An issuer withhigh illiquidity in their bonds that has a low number of bondsoutstanding translates into sparse data sets for AI algorithms to trainon. For example, as shown in FIG. 2E, Apple™ (first quadrant below) hasbid/ask recorded in most of the tenors across the curve.

Price analytics engine 34 handles the problem of sparse data sets byfilling the data gaps with balance sheet fundamentals and primary newissue quotation pricing levels to arrive at best fit or relative-valueprice for secondary market securities. Companies with only a minimalhistorical data available from secondary market trades of their bondsare enhanced with indicative new issue pricing curves and fundamentalsto successfully generate yield curves across all tenors. Price analyticsengine 34 finds observable secondary trade data-points during thepricing coverage period.

Illiquid Companies with only minimal trading activity may have modeledand relative-value prices for secondary market securities across alltenors. Their sparse data sets are enhanced with data from its peers, asdetermined in phase two of the algorithm. Human oversight ensures theoutput from price analytics engine 34 is accurate by regularly retuningthe machine learning algorithm to maintain a minimized mean absoluteerror (MAE) with respect to the new issue prices available in themarket.

In one implementation, price analytics engine 34 builds a spread curvefor bond issuers that issue frequently. The pricing model is designedfor issuers not for International Securities Identification Number(ISIN). To predict spread for a specific ISIN, a new sequential model isgenerated, however, the government model for pricing prediction.

Each ISIN has daily pricing information (i.e. bid yield) since itsorigination until now. Spread can be obtained by subtracting benchmarkyield from ISIN's bid yield. Based on the historical spread, a timeseries model is developed to predict the future spread next day or nextweek. The model development process consists of 2 components: (1) agovernment model for estimating benchmark yield based on tenor on aspecific trade date; (2) a time series model to predict spread for eachISIN based on historical spread.

Given an ISIN's trade date, historical bid yield for the governmentbonds that are issued in the same currency as the ISIN's are extractedand tenor is calculated as the number of years between trade date andmaturity date for each government bond. To estimate the benchmark bidyield based on tenor, a government model using support vector regressionto discover the yield curve shape is built. The input variable is tenorand target variable is government yield.

To calculate the ISIN'S spread on that trade date, the maturity date isused to calculate the ISIN's tenor, and assign a tenor to the governmentmodel and then a benchmark yield is projected from the government model.Using this benchmark yield, the spread on that trade date can becalculated.

In one example, as shown in the plots of FIGS. 2F and 2G, historicalspreads for each ISIN XS1849464323—Playtech Plc and ISINXS1700435453—Banaca IFIF SpA have their unique trends and volatility andthus different types of models are applied to them.

Price analytics engine 34 builds a yield curve for bonds issued bygovernment, leverages this data to calculate benchmark yield forspecific tenor of each ISIN from this curve and then subtract this fromISIN's bid yield to get spread. After obtaining historical spread, amachine learning algorithm is employed to build a model to predictspread in the future. To validate the model performance, the data set isseparated into training and testing data set.

In one example, some ISIN were selected and pre-processed to providedata for model training. One such ISIN was XS1849464323—Playtech Plc,and the model implemented for this ISIN was Bidirectional LongShort-term Memory (BLSTM) Neural Network, a recurrent neural networkcapable of learning long-term dependencies, and processing therelationship among historical observations in both forward and backwarddirection. The bidirectional aspect of BLSTM is especially useful forpredicting spread since future spread is usually affected by historicaldata. In one example, the time step for each training step is 30 days,i.e. the model uses information in the past one month and recognizespatterns in the past to predict spread next day. The mean absolutepercentage error of training data was 3.64% and that of testing data is3.076%. Actual and prediction for both training (in time) and testingdata (out of time) is shown in the graphs of FIGS. 2H and 2I,respectively.

In another example, ISIN XS1700435453—Banaca IFIF SpA is selected, andthe model implemented for was Autoregressive Integrated Moving Average(AMNIA). AMNIA is class of model that captures a suite of differentstandard temporal structures in time series data, and uses the dependentrelationship between an observation and some number of laggedobservations i.e. autoregression. The model also uses differencing oforiginal observations (e.g. subtracting an observation from the previousobservation) in order to make the time series stationary i.e.integration; and the model uses the dependency between an observationand a residual error from a moving average model applied to laggedobservations. For this ISIN, the number of lagged observations is 1, thenumber of times that observations are differenced is 2 and the size ofthe moving average window is also 1. The mean absolute percentage errorof training data is 1.355% and that of testing data is 1.103%. Actualand prediction for both training (in time) and testing data (out oftime) is shown in the graphs of FIGS. 2J and 2K, respectively.

As be seen from the results shown in FIGS. 2H, 2I, 2J, and 2K, thesequential model is able to accurately capture the trend of how spreadchanges in the future. The same method and analysis can be generalizedto other ISINs of interest to make prediction on spread.

FIG. 2L shows user interface 3100 the pricing spreads for variousissuers 3110. As can be seen, each issuer 3110 is associated with asector 3112, ratings 3114, spread to GoC curve (bps) 3116, andassociated curves 3118.

FIG. 2M shows user interface 3200 with modeled yield curve 3210 for aparticular issuer 3212.

FIG. 3A shows predictive issuance analytics engine 36 for systematicallyidentifying at least one fixed income market opportunity, wherein the atleast one fixed income market opportunity comprises a financialinstrument, and to provide pre-issuance insights into the fixed incomemarket. Accordingly, predictive issuance analytics engine 36 comprises asuite of predictive analytics 101 tools that provides users with uniquemarket insights that are currently not available. Platform 10 utilizesstate-of-the-art technologies to provide market insights that usersrequire to gauge market conditions and to participate in the primarymarket. New Issue Level Prediction 300 uses machine learningtechnologies on vast sets of data, such as current and historicalsecondary market trading levels of correlated securities, to predict newissue levels of specific issuers. This predictive analytics toolcomplements pricing indications produced primarily by dealers to providean objective, data-driven view of market levels. The machine learningprocess produce New Issue Level Prediction is illustrated on FIG. 12.Platform 10 utilizes supervised learning methods and aggregates andcleanses data into a structured data sets to extract features 1200vectors for the purposes of training machine learning algorithms 1201.

Examples of features include current and historical levels and detailsof new securities issuances, current and historical secondary tradinglevels of bonds, equities, and derivatives, credit ratings, sectorinformation, financial metrics such as leverage ratios, and marketindicators. Multiple types of regression models are employed insupervised with feature vectors generated through data aggregation andprocessing units. This process is iterated on a continuous basis usingnew feature vectors to test predictions and continuous training of themodel. The outputs include theoretical clearing new issue levels forsecurities, implied secondary levels, and implied new issue premiums.Similarly, Issuance Propensity Prediction 301 tool uses aregression-based machine learning algorithm to predict the propensity ofa specific issuer to access the new issue market in a given timehorizon. This is particularly useful for dealers looking to focus theirefforts on providing investment banking coverage to assist with futureofferings; investors looking to focus their human resources on analyzingand engaging with issuers that are likely to issue securities in thenear future; and any service providers looking to provide solutions orproducts geared towards primary market activities can use the results astheir sales leads. Refinancing Probability Prediction 302 is a variationof the Issuance Propensity Prediction to provide users with theprobability of refinancing occurring for each security coming due in agiven period of time. Such information is useful to gauge potential newissue supply over a specified time horizon. Investors holding a maturingsecurity can use this information to participate in the refinancingevent. Investor Demand Prediction 303 analyzes data sets supplied intoplatform 10 as well as generated within platform 10 to provide users,particularly dealers and issuers, with a prediction of potentialaggregated investor demand for a specific potential new securitiesissuance. The machine-learning algorithm uses multiple feature vectorssuch as recent deal participation metrics, deal types, bond holdingsdata, investor activities, and market indicators to provide predictiveinsights. Lead Dealer Prediction 304 employs an algorithm to studyhistorical dealer-issuer relationships such as past deal syndicatestructure, lending relationships, and other relevant information topredict the likely dealer to lead the next new issue offered by aspecific issuer. Multiple types classification models are trained andtested prior to integrating into platform 10. FIG. 13 illustrates anoverview of classification-based model training. Such information isparticularly valuable for users looking to select counterparties toengage with, in the context of primary capital market.

Predictive issuance analytics engine 36 comprises machine learningalgorithms systematically identify highly likely new bond issuancesglobally, providing exclusive pre-issuance insights into the fixedincome market, identifying new-supply unidentifiable by prior analyticalmethods. Platform 10 predicts the most optimal indicative new issue, itsbond price as well as relative value secondary market bond price forglobal investment-grade (IG) and high yield (HY) issuers globally,utilizing machine learning (ML) algorithms. Generally, the ML algorithmsanalyze millions of data points related to factors such as secondarylevels, recent indicative new issue price quotations, foreign exchangeswap costs, company fundamental data elements, investor sentiment andsector comparable. Additionally, the model scores secondary bonds acrosspredefined currencies e.g. all G10 currencies and prices thecross-currency basis swap in all G10 currency pairs. The total costbenefit is optimized to find cheapest issuance/purchasing price andlocation. Predictive issuance analytics engine 36 comprises an AIalgorithm family which makes ongoing measurements of issuer's propensityto issue bonds, and assigns a score which estimates the relativelikelihood a bond issuer will come to market with bonds in the nearfuture. Predictive issuance analytics engine 36 analyzes factors frommultiple types of data sources including: bond market data, such astransactions occurring in the secondary market, and historical issuancespreads; investment banking data, such as fundamentals on corporations,their balance sheet indicators, proprietary data sets treasury groups ofthe corporations themselves had on file such as dealer quotations andtrade points; and proprietary data, such as data derived from directaccess to large community of issuers and institutional investors viaestablished feedback loops.

In one example, the predictive time horizon the predictive issuanceanalytics engine 36 in standard use cases is optimized to four weeks. Ascore is assigned for each company in each potential bond issuancetenor. Scores are on a scale of 0-100 and are relative to other issuersand other bond issuance tenors. A higher score in general means thatcompany is more likely to issue in that tenor compared to a company or atenor that receives a lower score. High scores (˜70-80 or higher) acrossall tenors imply that an issuer is likely to issue a bond in any tenor.High scores in only one tenor imply that the issuer is more likely toissue in that tenor compared to other tenors. Low scores across alltenors imply that the issuer is less likely to issue in any tenor. Thepropensity scores are indicative of the probability of issue. Forexample, a score of 90 means that issuer is in the 90th percentile in aranking against all other companies in all other issuance tenorpossibilities.

In more detail, FIG. 3B outlines the exemplary steps of the predictiveissuance analytics engine 36 executed by processor 12, for predicting ifan issuer will come to the market and issue bonds. A variety ofpre-processed inputs are fed into predictive issuance analytics engine36 algorithms, to predict issuances. For example, the following is asubset of indicators used, namely: Sector Specific Indicators i.e.Spread Compression Relative to Sector: In a situation where the spreads(bond valuations) in a specific sector have compressed relative to othersectors, issuers could capitalize on lower spreads, which translates tolower cost of borrowing, by coming to market and issuing bonds; andPopular Sector for Issuance: Companies in sectors which issue bondsfrequently are more likely to issue.

Issuer Specific Indicators comprise recent issuance and refinancing needseasonal/monthly issuance overdue issuance, prospectus filing, spreadcompression relative to self. With respect to recent issuance, if acompany has issued bonds recently, they may be less likely to come tomarket soon. Predictive issuance analytics engine 36 tracks recentissuances on a monthly time horizon, quarterly time horizon, and yearlytime horizon. With respect refinancing need, an issuer's sources offunding and uses of funds are analyzed to determine if an issuer has aneed for funding. Generally, issuers are more likely to issue if theirfunding position is negative. Accordingly, refinancing need is analyzedon a monthly, quarterly, and annual basis.

With respect to seasonal/monthly issuance, if an issuer tends to issueduring certain months or seasons, they may continue to follow a similarpattern. An example underlying cause for a pattern in their issuanceswould be blackout periods.

With respect to overdue issuance, an issuer who regularly issued acertain number of bonds and amount of debt in previous years may issuethe same number of bonds and amount of debt in the current year.Deviation from regular issuance pattern in current year versus pastyears in the sample set is measured and correlations are identified notonly with respect to that particular issuer but their sector peerissuers as well.

With respect to prospectus filing, an issuer has recently submitted aprospectus to securities regulators indicating they are seeking to raisecapital.

With respect to spread compression relative to self, monitoring ifspreads of an issuer have compressed compared to its indicative.

Predictive issuance analytics engine 36 sources raw trading andfundamental data via automated scripts executed at predeterminedintervals e.g. every 24 hours (step 4000). The data sources includeThomson Reuters (primary and secondary bond issuance and trading levels,secondary pricing data, outstanding securities, historical bondissuance), S&P Global Market Intelligence (company fundamental data),DBRA, S&P, Moody's, Fitch (company ratings, company credit rating, andmacro market data), Thomson Reuters (company sector information), SEDAR(Canada), EDGAR (USA), public filings (prospectus filings), CentralBanks/Treasuries, public sources (Macro Market Data), including variousother sources. This raw data is pre-processed (step 4002) and thetrading data and fundamental data is structured and mapped to theappropriate issuer ID, and stored in databases 27 (step 4004). The datais systematically scrubbed for anomalies and null values. Finally, a setof key input factors are generated based on the raw input. These includebut are not limited to factors that measure recent issuance, issuancefrequency, maturity schedule gap, propensity for specific tenors (step4006). These factors are divided between sector and company specific andare used as inputs to the machine learning models.

The structured data is fed into the machine learning algorithm astraining data to generate several models (step 4008) to calculate theoutput propensities. These models are each trained using a subset of thepast data, ranging from one month to a maximum of ten years. Multiplesupervised machine learning algorithms are trained using past data topredict issuances, such as, XGBoost, Neural Networks, Random Forest, andLogistic Regression. As an example, the XGBoost algorithm is able toautomatically handle missing data values, and therefore it is sparseaware, includes block structure to support the parallelization of treeconstruction, and can further boost an already fitted model on new datai.e. continued training.

Advanced sampling techniques were used to account for class imbalancebetween positive (will-issue) and negative (will-not-issue)predictions). Finally, the results are back-tested against the entireten years of data and measured for precision and recall metrics.Predictive issuance analytics engine 38 uses a robust ensemble method tocombine the results from each algorithm and generate an output score.This score represents the propensity of an issuer to issue a bond in aspecific tenor i.e. a propensity score (step 4010).

As an example, predictive issuance analytics engine 36 outputs issuancepropensities for each tenor (2, 3, 5, 7,10, and 30 years) for eachissuer and in each currency that they issued in before. Predictiveissuance analytics engine 38's propensity score represents ‘LikelihoodTo Issue’ in next 4 to 6 weeks and is outputted with strongestunderlying market signals that contributed overall to algorithm issuancerecommendation.

FIG. 3C shows exemplary user interface 4020 with an output issuancerecommendation for one issuer. User interface 4020 comprises predictionsignals 4022 for a new issue, such as, maturity schedule gap, investmentfit, overdue issuance, tenor propensity, and upcoming debt maturity andissuance propensity. Prediction signals 4022 indicate the underlyingreasons behind the score associated with the particular issuer. A listof new opportunities 4024, including expiration data 4026, predictionsignals 4028 and reverse inquiry buttons 4030, are also presented.

FIG. 3D shows exemplary user interface 4040 with sample dashboardcomprises issuance propensities tab 4042 for tracking multiple issuanceopportunities on platform 10. As can be seen, each issuer 4050 isassociated with a sector 4052, ratings 4054, and issuance prediction4056.

FIG. 3E shows a sample back-tested performance of the algorithms ofpredictive issuance analytics engine 36, plotting predictions for aspecific issuer to issue bonds in a specific tenor, in one example.Propensity values are plotted over time, with black bars representingwhen actual issuances have occurred. The gray area trailing eachissuance on the first graph indicates the issuance prediction window. Inthis example, the default time horizon for the propensity issuanceprediction is four weeks, however, the time horizon may be adjusted asdesired.

Taking above specific issuance prediction back-test, a furthersystematic back-test on a basket of 600 issuers is performed, testing 6standard issuance tenors for 500 weeks, representing around 1.1 millionpredictions. For each issuer and every tenor predictive issuanceanalytics engine 36 calculates the likelihood to issue every week. Theoverall result predictions were categorized in 4 buckets: Highly Likelyto Issue, Likely to Issue, Unlikely to issue and Very unlikely to Issue.As can be seen in the table of FIG. 3F, the majority of issuers/tenorsthat algorithm predicted as being highly likely to issue did in factissue, above 81% precision. Similarly the vast majority of theissuers/tenors that algorithm predicted as being very unlikely to issuedid not issue. This is true for all issuer industry sectors and ingeneral.

Furthermore, the propensity output may be presented in two formats:historical and current. Historical propensity is given as a separatetime series going back two to five years for each tenor. (2, 3, 5, 7,10, and 30 years) for each issuer. FIG. 3G shows an output graph for thehistorical propensity for a single issuer and single tenor prediction,with the time across the x-axis, and the propensity score on the y-axislabeled as ‘Likelihood to Issue’. The graph also shows black verticalbars at the dates where that issuer actually issued in a given tenor.

Current propensities can be supplied on a weekly basis, althoughfrequency can be scaled according to a client use case need. Forexample, pre-deal analytics applications in investment banking usuallyrequire one month or longer time horizon models optimization while usecases in fixed income trading world often entail model optimizationsthat are as close to real-time as possible. In addition, platform 10exposes underlying factors which would be commonly understood byanalysts to contribute to a propensity score at any given time. Breakingdown the propensity scores into more detailed categories, factorsinclude: Upcoming Maturity; Average Maturities per Year; OverdueIssuance; Popular Sector for Issuance; Recent Issuance, etc. In noneexample, predictive issuance analytics engine 38 comprises a non-linear,non-parametric algorithm, and the overall propensity scores are notdirectly proportional to a weighted average of the sub-scores. Thesesub-scores are intended to give a deeper level of explain-ability to theindicators used to derive the propensity scores.

FIG. 3H illustrates matching and discovery engine 38 for matching atleast one target institutional buyer with the fixed income marketopportunity. Similarly, Potential Issuer Recommendation 305, PotentialDealer Recommendation 306, and Potential Investor Recommendation 307provides users with the ranking of relevant counterparties topotentially engage with, given a set of criteria such as productspecialties, capabilities, demand, and investment track records. Forexample, a new securities issuer can use the Potential InvestorRecommendation 307 tool to generate a list of investors that are likelyto invest in the issuer's new offering, based on data-drivenpredictions. Platform 10 provides both explicit rule-basedrecommendations as well as recommendations based on algorithm trainedthrough supervised training. Furthermore, the Existing New Issue BuyersAnalysis 308 tool allows users, specifically issuers and dealers, toupload the buyer list of historical new issues. Platform 10 analyzes thebuyer list and provides an optimized list of potential investor matches.The optimization is configurable based on pre-determined feature vectorsto arrive at the most relevant matches. The result uses a system thatalso incorporates public information such as investor holdings, investormandates and sector preferences, and investor historical new issueparticipation patterns.

In more detail, discovery and matching engine 38 provides analyticsplatform for issuers, dealers and investors to discover traditional andnon-traditional buyers for new bond issuances as well as profilingpricing tension in secondary market and risk appetite for target buyers,enabling systematic opportunity monitoring and market signal alerts.

Discovery and matching engine 38 employs algorithmic matching of targetbuyers with fixed income opportunities, based on past buying patterns,portfolio manager preferences, rebalancing events and preferred industrysector, rating or tenor. Discovery and matching engine 38 is an advancedAT algorithm family which makes ongoing observations of investorbehavior, buying-patterns and rebalancing events, and identifies a setof traditional and non-traditional buyers for each market creditopportunity. Discovery and matching engine 38 analyzes features focusingon data variables below:

sector concentration: An investor with higher transaction volume and/orlarger holdings in a specific industry sector is ranked higher whenmatching opportunity has issuer from the same sector. For example, ifissuer is in the energy sector, opportunity is more likely to be matchedwith an investor who recently executed larger number of transactions inenergy bonds;

cross-currency classification: discovery and matching engine 38considers the currency in which the investor's holdings are denoted as aranking criterion. Investors who hold higher levels of GBP securitiesfor example are ranked higher if the trade opportunity identifies issuerwho is also expected to issue GBP denominated bonds;

credit rating profile: Discovery and matching engine 38 gauges aninvestor's risk tolerance by considering the quantity ofinvestment-grade to high-yield bonds in the investor's portfolio.Issuers with lower credit ratings are more likely to be matched withinvestors whose portfolios hold more high-yield securities; and

traditional/non-Traditional Investors: An investor with continuousholdings and prior transactions in bonds of the corresponding issuer islabelled as a traditional investor for opportunities of that issuer(credit type, currency, rating, industry sector). Investors without thispast buying pattern are considered non-traditional.

In one example, discovery and matching engine 38 analyzes more than2,900 investors' portfolios and ranks the investors' interest based ontheir existing holdings and quarterly rebalancing. Using the algorithms,issuers or dealer underwriters acting on their behalf can systemicallyidentify investors who are traditional and non-traditional buyers.

In more detail, FIG. 3I outlines the exemplary steps of the discoveryand matching engine executed by processor 12, for identifying a set oftraditional and non-traditional buyers for each market creditopportunity.

Discovery and matching engine 38 sources raw trading and fundamentaldata via automated nightly scripts (step 5000). This raw data ispre-processed (step 5002) and the trading data and fundamental data isstructured and mapped to the appropriate issuer ID, and stored indatabases 27 (step 5004). The data is systematically scrubbed foranomalies and null values. Finally, a set of key input factors aregenerated based on the raw input. These include but are not limited tofactors that measure sector concentration, cross-currency classificationof different investor types, credit rating profile investor preferenceand traditional/non-traditional investors (step 5006). Discovery andmatching engine 38's primary additive data input is eMAXX Investorholdings data sourced from Thomson Reuters. Discovery and matchingengine 38 sources raw data from major data suppliers in the financialsector, including Thomson Reuters, S&P Global Market Intelligence, majorcredit rating agencies, proprietary sources, as well as other sources.The data that discovery and matching engine 38 algorithms use includesthe following: eMAXX Investor Holdings Data, Investors, InvestorInsights Campaign, Secondary Pricing Data, Outstanding Securities,Historical Bond Issuance, Fundamental Data, Issuer Credit Rating,Industry Sector Information, Prospectus Filings and Macro Market Data. Adata refresh is performed quarterly and an algorithm monitors anychanges in the investors' holdings data table. eMAXX data bundlesprovide issuer/investor data, security classification, and credit ratingdata which are pre-processed before they are inputted into thealgorithm.

The subsequent stage for the machine learning algorithm is to train andapply several models to calculate the output investor relative matchscores. These models are each trained using a subset of the past data,ranging from one month to a maximum of ten years (step 5008). In oneexample, feedback loops for machine learning are established throughinvestor insights campaign that runs monthly and sources on average 4billion USD in non-executable investor credit preferences (acrosscorporate, sovereign, supra-sovereign, municipal and provincial issuercredit). The results are back-tested against the entire ten years ofdata history and measured for precision and recall metrics, and issuerbond investors profile and the supply discovery opportunities areoutputted (step 5010).

In addition, discovery and matching engine 38 ranks each investordepending on their likelihood of investing in a security with thepredefined criteria. FIG. 3J shows user interface 5100 showing bondbuyer matching results from discovery and matching engine 38, accessedvia actuation of tab 5102. The ranking is based on the quantity theinvestor 5104 currently has invested based on the inputted criteria,number of prior transactions in relevant category, and notional size ofpurchasing activity, shown generally as 5106. As an example, an investorwith high amount of USD bonds in their portfolio will be ranked higherwhen the issuance opportunity is USD denominated. The investor rank 5108(outputted as number of stars beside investor organization name 5104)represents the quintile in which the investor ranks after discovery andmatching engine 38 ranking algorithm finished the analysis (i.e. aninvestor in the upper quintile will show five stars while an investor inthe lower quintile will show one star).

Using issuer credit type characteristics, discovery and matching engine38 first identifies investors who are traditional buyers. Once theseinvestors are identified and ranked, algorithms identify non-traditionalbuyers based on currency, rating or industry sector buying preferences.Each prospective investor is ranked based on the contents of theirportfolio, frequency of their buying patterns, expressed preferences andrebalancing.

FIG. 4 provides an overview of various communication tools 102 thatprovide users with multiple, user-friendly, channels of communications.The Live Chat 400 module allows users to communicate instantly withother users on platform 10. The module provides secure communicationover HTTPS and may include end-to-end encrypted messaging services.Platform 10 is also offered through a Single Page Application interface,which allows for advanced notification and chatting capabilities. Forexample, users are able to have multiple chat boxes within associatedweb pages of platform 10. This is particularly useful as users are ableto communicate seamlessly without leaving a page, staying within thecontext of each workflow. The Live Chat 400 module also supports groupchatting to facilitate multi-user meetings such as syndicate groupmeetings. The Expression of Interest 401 tool, is an invention thatallows users to communicate pre-deal interests (“Reverse Inquiries”)with each other. Specifically, investor users are able to select targetissuers, fill in inquiry details such as tenor, interest size,structure, currency, pricing levels, expiry date, and others. Users withpre-approved permissions are also able to create Reverse Inquiries onbehalf of other users. For example, an issuer user may receive a ReverseInquiry through the form of electronic email. They may forward theelectronic mail to a secure mailbox maintained by platform 10—theReverse Inquiry will be logged on the platform for recordkeepingpurposes. Similarly, dealer users may act on behalf of their clients onboth investor and issuer side to reflect latest developments. Users arealso able to see a clear audit trail of creation of digital ReverseInquiries and any modifications. Users are also able to communicatethrough a secure channel with the appropriate counterparties. Investorusers have the option to submit Reverse Inquiries anonymously and mayshare the inquiry details with dealers intermediating the potential newissue. All platform 10's specific features are customizable subject to auser's permissions. These permissions are determined from the variousregulations applicable to a given user, and thus prevent users fromperforming certain activities. Order Book Communication 402 allows usersto securely communicate the state of a live or closed order book withother relevant counterparties. This is an essential part of the bookbuilding process. Indicative New Issue Quotes Communication 403 andIndicative Market Level Communication 404 modules enable various typesof all-to-all market intelligence communication as stated for DigitalNew Issue Level Indications 200. Platform 10 allows for privilegecontrols for internal team members. For example, platform 10 can beconfigured so that a Debt Capital Markets Analyst (dealer user) may onlypublish Pricing Indications for a specific list of issuer clients.Secondary Market Trade Point Communication 405 allows users to upload,manage and communicate their trading activities with theircounterparties. Further, the Event & Meeting Request Management 406tool, another embodiment of the invention may include a digital tool torequest and schedule meetings with counterparties in the primary capitalmarket. Notifications and Alerts 407 is a built-in system that allowsusers to seamlessly be alerted through a variety of channels including,but not limited to, in-application notification and alerts, emailnotifications, Short Message Service (SMS) messages, and automated phonemessage. The notification management system allows for both internal andexternal events tracking, customizable cadence, and automaticsubscription based on user roles through integration with permissionaccess control. Lastly, platform 10 enables multiple Integration 408capabilities for all modules. Examples of integration includes thefollowing: Outlook® email software integration, chat channel integrationwith client-side servers, and data integration with client-side serversvia APIs.

Referring to FIG. 5, an embodiment of the invention includes a suite ofrelationship management 103 tools. A user of platform 10 is able tomanage aspects of relationships with counterparties on platform 10.Through the Historical Deal Participation 500 tool, users trackhistorical records of deal participation using a private databasemanagement system. Through Dealer-Issuer Relationship Strength Report501, Dealer-Investor Relationship Strength Report 502, andIssuer-Investor Relationship Strength Report 503 modules, a system usercan gauge the state of the relationship maturity with a given primarymarket counterparty. For example, an issuer user may generate a multiasset class capital markets relationships report to analyze the overallrelationship summary and scoring with each of its dealer counterparts.Similarly, an investor user may generate a multi asset class capitalmarkets relationship report to analyze the overall relationship summaryand scoring with each of its dealer counterparts. Private Archive ofMeetings, Call Reports, and Research Notes 504 may be maintained byusers to streamline recordkeeping and financial analysis. RelationshipEnhancement Tools 505 promote fostering good relationships betweendealers, investors and issuers through communication channels, such asemail and messaging. Lastly, platform 10 enables multiple integration506 capabilities for all modules. Such integration includes thefollowing: Outlook® email software integration, chat channel integrationwith client-side servers, data integration with client-side servers viaAPIs.

FIG. 6 is an overview of systems to aggregate, process, and integratedata from multiple sources into and out of a centralized dataaggregation, management, and processing 104, in the context of primarycapital markets. Platform 10 relies on abstract APIs and a data feedconsuming pipeline capable of interoperating with multiple capitalmarket feeds real-time. Furthermore, market data is stored and cached,with millions of data points in a unified document architecture.Platform 10 contains a corresponding database/document architecture andcaching layer for the persistence of serving market data to internalapplication components. Further, platform 10 may employ Redis™ softwareclusters with multiple replicas to minimize web latency. Internalapplication abstraction from market data feeds is achieved through aninternal micro-services architecture. Verification of federated datascalability is achieved through load-testing for both concurrent usersand data volumes. Data Aggregation Hub 605, which interfaces withmultiple data providers including Digital Indicative Pricing and MarketLevel Communication 600, Uploaded Document 601, Documents Sent toPrivate Electronic Mailbox 602, Application Program Interfaces 603, andPublic Data 604. As well, Network Data 608, metadata generated by theusage of platform 10 is transmitted back into the Data Aggregation Hub605 to further enrich data quality and supply information needed forvarious predictive analytics modules. Data Processing Automation Unit606 formats, filters, and reconciles records prior to being transmittedfor use by system users via Information Creation 607. In addition,advanced natural language processing techniques may be utilized tosynthesize, extract keywords (for example, unique identifiers, companyname), perform sentiment analysis, and extract transaction related termssuch as covenants and legal term definitions. Platform 10 allows forClient Systems Integration 609, providing the ability to synchronizedata sets with users' internal systems through secure applicationprogram interfaces.

FIG. 7 is an overview of a customizable digital deal execution workflow110 with permission-controlled access provided to multiple stakeholders700. In embodiments of the invention, Pre-Deal Demand conversion 701 isa step-by-step process that allows the issuer and/or dealer to CreateDeals 702 and populate it with existing Reverse Inquiries and continuethe process of gauging interest by Soft Sounding 703, Opening an OrderBook 704, Closing Order Book 705, Allocating 706, AllocationConfirmation 707 with Investors, Pricing 708 and confirming SettlementInstruction 709 to reach settlement 710. The deal execution moduleprovides highly customizable deal execution workflow and permission setsfor different users.

Referring to FIG. 8, embodiments of the invention may include a modulethat applies Customizable Proprietary Order Book AllocationMethodologies 111 to manage and allocate order books. An order book maybe populated by investor and dealer users through the Order Submission800 interface. Investors may share order information internally toaggregate orders from multiple portfolio managers and to facilitate astreamlined order approval process 801, 802, 803 for Order BookFormation 804. Algorithm used for the Allocation Tool 806 may useinformation such as order size, relationship strength report, predictivemetrics, and order submission time to provide suggested allocationamounts for each order. The Allocation Confirmation 807 tool may includereal-time, two-way communication between the syndicate and investors tomaintain the latest status of the order book. The tool may be extendedto include an investor-side internal allocation protocol 808 to furtherallocate the new issue within an investor organization.

FIG. 9 is an overview of customizable digital deal execution 112workflow with permission-controlled access provided to multiplestakeholders. Platform 10's Post-Deal Best Execution Analytics 905provides reporting solutions that generate reports and metrics onspecific new issue execution by taking into account multiple data pointsincluding Multiple Market Secondary Bid and Offer Data 900, New IssueTransaction Data 901, Transaction Cost Data 902, Cross Asset ClassMarket Data 903, and other Public Data 904. The Best Execution algorithmincludes weighting on various pricing trends to identify usefulstatistics such as theoretical clearing pricing levels, optimal dealerselection, and optimal market timing. Further, the module providesregulatory compliance reports for a given new issue trade.

FIG. 10 is an integrated suite of tools that allow users to managemultiple financial documents 113 including Offering Documents 1000,Rating Agency Reports 1001, Financial Reports 1002, Private AnalysisReports 1003, and Deal Files 1006. The module may include completeindexing and searching capabilities 1004 to provide seamless access torelevant documents. The module leverages tools for financial documentsthat may be built on top of Apache Lucene® to provide real-time indexingand faceted search of all document contents. As well, access todocuments are controlled through customizable Sharing and PermissionControl 1005 protocol built into platform 10. Lastly, platform 10enables multiple Integration 1007 capabilities for all modules. Suchintegration includes the following: integration with client-sideservers, data integration with client-side servers via APIs, file backupprotocols through client side server integration.

FIG. 11 is a suite of tools available for users to comply with therelevant regulatory compliance 114 requirements. The module iscustomizable to allow users to meet with relevant regulatory complianceand reporting mandates. These include complete Audit Trail 1100 of alluser activities, built-in Deal-tailored Compliance Mandates 1101,Actionable Tasks Monitoring 1102, customizable Compliance Checklist1103, and Know Your Client (KYC)/Know Your Product Compliance Management1104, Advanced Permission Control 1105 and Data Files Delivery andReceipt 1106. The tool allows users to generate regulatory reportsrelated to non-deal and deal-related activities of all user types. Theinvention may also contain direct Integration 1107 service withclient-side servers.

FIG. 12 depicts an overview of continuous supervised machine learningprocess according to an embodiment of the invention using regressionmodels for the purpose of Digital New Issue Indication predictiveanalytics tool. Accordingly, as with the preceding descriptions inrespect of FIGS. 1 to 11 platform 10 relies on abstract APIs and a datafeed consuming pipeline capable of interoperating with multiple capitalmarket feeds real-time. Market data is stored and cached, with millionsof data points in a unified document architecture and platform 10contains a corresponding database/document architecture and cachinglayer for the persistence of serving market data to internal applicationcomponents. Internal application abstraction from market data feeds isachieved through an internal micro-services architecture. Accordingly,the process flow depicted exploits the Data Aggregation Hub 605 asdescribed supra in respect of FIG. 6 which provides the Data ProcessingAutomation Unit 606 with the data and supply information necessary forit to perform the required predictive analytics. Accordingly, Features1200 defines the features of the regression sought and these togetherwith the Labels 1203 are coupled to the Machine Learning Algorithm 1201to generate the analytic algorithm between the features identified.Based upon the execution of the Machine Learning Algorithm 1201 themodel is tested, for example through regression analysis, and exploitedto make predictions with Model Testing (Regression) & Predictions Module1202. At this point the output from the model testing and predictionsare fed-back to the Data Aggregation Hub 605 allowing the process toiterate and exploit new data during these subsequent iterations.

Now referring to FIG. 13 there is depicted an overview of continuoussupervised machine learning process according to an embodiment of theinvention using regression models for the purpose of Digital New IssueIndication predictive analytics tool. Accordingly, as with the precedingdescriptions in respect of FIGS. 1 to 12 platform 10 relies on APIs anda data feed consuming pipeline capable of interoperating with multiplecapital market feeds real-time. Market data is stored and cached, withmillions of data points in a unified document architecture and platform10 contains a corresponding database/document architecture and cachinglayer for the persistence of serving market data to internal applicationcomponents. Internal application abstraction from market data feeds isachieved through an internal micro-services architecture. Accordingly,the process flow depicted exploits the Data Aggregation Hub 605 asdescribed supra in respect of FIG. 6 which provides the Data ProcessingAutomation Unit 606 with the data and supply information necessary forit to perform the required predictive analytics. Accordingly, Features1300 defines the features of the regression sought and these togetherwith the Labels 1303 are coupled to the Machine Learning Algorithm 1301to generate the analytic algorithm between the features identified.Based upon the execution of the Machine Learning Algorithm 1301 themodel is tested, for example through regression analysis, and exploitedto make predictions with Model Testing (Classification) & PredictionsModule 1302. At this point the output from the model testing andpredictions are fed-back to the Data Aggregation Hub 605 allowing theprocess to iterate and exploit new data during these subsequentiterations.

Examples of models employed within the Machine Learning Algorithm1201/1301 and as exploited for model testing, regression and predictionsmay include, but are not limited to, linear regression, polynomialregression, general linear model, generalized linear model, discretechoice, logistic regression, multinomial logit, mixed logit, probit,multinomial probit, Poisson, multilevel model, fixed and/or randomeffects, non-linear regression, non-parametric, semi-parametric, robust,quantile, isotonic, principal components, local segments, anderrors-in-variables. Examples of estimation models employed within theModel Testing (Regression) & Predictions Module 1202/1302 and asexploited for model testing, regression and predictions include, but arenot limited to, least squares, partial, total, generalized, weighted,non-linear, iteratively reweighted, ridge regression, least absolutedeviations, Bayesian, and Bayesian multivariate.

Referring to FIG. 14 there is illustrated a table of financial data andrates as employed commonly within financial transactions and decisionmaking. As depicted the table presents several standard factors, namelyMaturity, Benchmark, Benchmark Yield, Re-Offer Spread, Re-Offer Yield,and Swapped Spread relative to US$ London Interbank Offered Rate(LIBOR). Against each of these are depicted different financial“products” which in this instance are different term US Treasury bondsfor each of the selected maturity terms, namely 5 year, 10 year, and 30year.

In one implementation, processor 14 may be embodied as a multi-coreprocessor, a single core processor, or a combination of one or moremulti-core processors and one or more single core processors. Forexample, processor 14 may be embodied as one or more of variousprocessing devices, such as a coprocessor, a microprocessor, acontroller, a digital signal processor (DSP), a processing circuitrywith or without an accompanying DSP, or various other processing devicesincluding integrated circuits such as, for example, an applicationspecific integrated circuit (ASIC), a field programmable gate array(FPGA), a microcontroller unit (MCU), a hardware accelerator, aspecial-purpose computer chip, Application-Specific Standard Products(ASSPs), System-on-a-chip systems (SOCs), Complex Programmable LogicDevices (CPLDs), Programmable Logic Controllers (PLC), GraphicsProcessing Units (GPUs), and the like. For example, some or all of thedevice functionality or method sequences may be performed by one or morehardware logic components.

Memory 16 may be embodied as one or more volatile memory devices, one ormore non-volatile memory devices, and/or a combination of one or morevolatile memory devices and non-volatile memory devices. For example,memory 16 may be embodied as magnetic storage devices (such as hard diskdrives, floppy disks, magnetic tapes, etc.), optical magnetic storagedevices (e.g., magneto-optical disks), CD-ROM (compact disc read onlymemory), CD-R (compact disc recordable), CD-R/W (compact discrewritable), DVD (Digital Versatile Disc), BD (BLU-RAY™ Disc), andsemiconductor memories (such as mask ROM, PROM (programmable ROM), EPROM(erasable PROM), flash ROM, RAM (random access memory), etc.).

I/O module 18 facilitates provisioning of an output to a user ofcomputing system 12 and/or for receiving an input from the user ofcomputing system 12, and send/receive communications to/from the varioussensors, components, and actuators of system 10. I/O module 18 may be incommunication with processor 14 and memory 16. Examples of the I/Omodule 18 include, but are not limited to, an input interface and/or anoutput interface. Some examples of the input interface may include, butare not limited to, a keyboard, a mouse, a joystick, a keypad, a touchscreen, soft keys, a microphone, and the like. Some examples of theoutput interface may include, but are not limited to, a microphone, aspeaker, a ringer, a vibrator, a light emitting diode display, athin-film transistor (TFT) display, a liquid crystal display, anactive-matrix organic light-emitting diode (AMOLED) display, and thelike. In an example embodiment, processor 14 may include I/O circuitryfor controlling at least some functions of one or more elements of I/Omodule 18, such as, for example, a speaker, a microphone, a display,and/or the like. Processor 14 and/or the I/O circuitry may control oneor more functions of the one or more elements of I/O module 18 throughcomputer program instructions, for example, software and/or firmware,stored on a memory, for example, the memory 16, and/or the like,accessible to the processor 14.

In an embodiment, various components of computing system 12, such asprocessor 14, memory 16, I/O module 18 and communications interface 20may communicate with each other via or through a centralized circuitsystem 22. Centralized circuit system 22 provides or enablescommunication between the components (14-20) of computing system 12. Incertain embodiments, centralized circuit system 22 may be a centralprinted circuit board (PCB) such as a motherboard, a main board, asystem board, or a logic board. Centralized circuit system 22 may also,or alternatively, include other printed circuit assemblies (PCAs) orcommunication channel media.

It is noted that various example embodiments as described herein may beimplemented in a wide variety of devices, network configurations andapplications.

Those of skill in the art will appreciate that other embodiments of thedisclosure may be practiced in network computing environments with manytypes of computer system configurations, including personal computers(PCs), industrial PCs, desktop PCs), hand-held devices, multi-processorsystems, microprocessor-based or programmable consumer electronics,network PCs, server computers, minicomputers, mainframe computers, andthe like. Accordingly, system 10 may be coupled to these externaldevices via the communication, such that system 10 is controllableremotely. Embodiments may also be practiced in distributed computingenvironments where tasks are performed by local and remote processingdevices that are linked (either by hardwired links, wireless links, orby a combination thereof) through a communications network. In adistributed computing environment, program modules may be located inboth local and remote memory storage devices.

In another implementation, system 10 follows a cloud computing model, byproviding an on-demand network access to a shared pool of configurablecomputing resources (e.g., servers, storage, applications, and/orservices) that can be rapidly provisioned and released with minimal ornor resource management effort, including interaction with a serviceprovider, by a user (operator of a thin client).

The benefits and advantages described above may relate to one embodimentor may relate to several embodiments. The embodiments are not limited tothose that solve any or all of the stated problems or those that haveany or all of the stated benefits and advantages. The operations of themethods described herein may be carried out in any suitable order, orsimultaneously where appropriate. Additionally, individual blocks may beadded or deleted from any of the methods without departing from thespirit and scope of the subject matter described herein. Aspects of anyof the examples described above may be combined with aspects of any ofthe other examples described to form further examples without losing theeffect sought.

The above description is given by way of example only and variousmodifications may be made by those skilled in the art. The abovespecification, examples and data provide a complete description of thestructure and use of exemplary embodiments. Although various embodimentshave been described above with a certain degree of particularity, orwith reference to one or more individual embodiments, those skilled inthe art could make numerous alterations to the disclosed embodimentswithout departing from the spirit or scope of this specification.

Benefits, other advantages, and solutions to problems have beendescribed above with regard to specific embodiments. However, thebenefits, advantages, solutions to problems, and any element(s) that maycause any benefit, advantage, or solution to occur or become morepronounced are not to be construed as critical, required, or essentialfeatures or elements of any or all the claims. As used herein, the terms“comprises,” “comprising,” or any other variations thereof, are intendedto cover a non-exclusive inclusion, such that a process, method,article, or apparatus that comprises a list of elements does not includeonly those elements but may include other elements not expressly listedor inherent to such process, method, article, or apparatus. Further, noelement described herein is required for the practice of the inventionunless expressly described as “essential” or “critical.”

The preceding detailed description of exemplary embodiments of theinvention makes reference to the accompanying drawings, which show theexemplary embodiment by way of illustration. While these exemplaryembodiments are described in sufficient detail to enable those skilledin the art to practice the invention, it should be understood that otherembodiments may be realized and that logical and mechanical changes maybe made without departing from the spirit and scope of the invention.For example, the steps recited in any of the method or process claimsmay be executed in any order and are not limited to the order presented.Thus, the preceding detailed description is presented for purposes ofillustration only and not of limitation, and the scope of the inventionis defined by the preceding description, and with respect to theattached claims.

1. A computer-implemented method for forecasting the pricing of at leastone financial instrument, the method comprising a processor and amemory, the method comprising the operations of: generating, by theprocessor, a user interface on a display, said user interface comprisinga user-selectable pricing tab; wherein selecting the pricing tab causesthe processor to at least: receive raw data in a plurality of disparateformats; scrub the raw data for anomalies and null values using a set ofrules and generate a structured data set; and wherein selecting saidpricing tab causes the processor to measure best-fit correlations withrespect to a company's fundamental valuation and secondary marketpricing for the company's at least one financial instrument acrosssector peers and market conditions and generate at least one financialinstrument pricing output, in real-time.
 2. The method of claim 1,wherein the processor extracts at least one feature vector set from thestructured data set.
 3. The method of claim 2, wherein the structureddata set is input into a machine learning architecture comprising aneural network, wherein the machine learning architecture generates amachine learning model using the at least one feature vector set.
 4. Themethod of claim 3, wherein the machine learning model is iterativelytrained to calculate relative value pricing curves for at least onecurrency.
 5. The method of claim 3, wherein the machine learning modelis iteratively trained to predict a price of the at least one financialinstrument within a time frame.
 6. The method of claim 1, comprising thefurther steps of: displaying, by the processor, on the user interface aplurality of user-selectable criteria comprising time thresholdspertaining to at least one of historical data, contemporaneous data, asector, a tenor, a bond rating, a model preference, a confidence leveland a liquidity score; displaying on the user interface the plurality ofuser-selectable criteria with selection indicators received by theprocessor to execute the instructions to forecast the pricing of thefinancial instrument.
 7. The method of claim 6, wherein the at least onefinancial instrument is a bond.
 8. The method of claim 1, wherein theuser interface comprises a user-selectable matching tab, whereinselecting said matching tab causes the processor to employ algorithmicmatching of target buyers with the at least one financial instrument,based on at least one of past buying patterns, portfolio managerpreferences, rebalancing events and preferred industry sector, rating ortenor; and to generate a ranking score indicative of the target buyer'slikelihood to purchase the at least one financial instrument.
 9. Acomputer-implemented method for forecasting the issuance of at least onefinancial instrument, the method comprising a processor and a memory,the method comprising the operations of: generating, by the processor, auser interface on a display, said user interface comprising auser-selectable issuance tab; wherein selecting the issuance tab causesthe processor to at least: receive raw data in a plurality of disparateformats; scrub the raw data for anomalies and null values using a set ofrules and generate a structured data set; and wherein selecting saidissuance tab causes the processor to perform measurement of at least onefinancial instrument issuer's propensity to issue the at least onefinancial instrument, and to assign a propensity score which estimatesthe relative likelihood the issuer will issue the at least one financialinstrument within a time frame; and output an issuance recommendationfor the issuer.
 10. The method of claim 9, wherein the processorextracts at least one feature vector set from the structured data set.11. The method of claim 10, wherein the structured data set is inputinto a machine learning architecture comprising a neural network,wherein the machine learning architecture generates a machine learningmodel using the at least one feature vector set.
 12. The method of claim11, wherein the machine learning model is iteratively trained tocalculate relative value pricing curves for at least one currency. 13.The method of claim 11, wherein the machine learning model isiteratively trained to predict an issuance of the at least one financialinstrument within the time frame.
 14. The method of claim 13, whereinthe at least one financial instrument is a bond.
 15. The method of claim9, comprising the further steps of: displaying, by the processor, on theuser interface a plurality of user-selectable criteria comprising timethresholds pertaining to at least one of historical data,contemporaneous data, a sector, a tenor, a bond rating, a modelpreference, a confidence level and a liquidity score; displaying on theuser interface the plurality of user-selectable criteria with selectionindicators received by the processor to execute the instructions toforecast the issuance of the financial instrument.
 16. The method ofclaim 15, wherein the user interface comprises a user-selectablematching tab, wherein selecting said matching tab causes the processorto employ algorithmic matching of target buyers with the at least onefinancial instrument, based on at least one of past buying patterns,portfolio manager preferences, rebalancing events and preferred industrysector, rating or tenor; and to generate a ranking score indicative ofthe target buyer's likelihood to purchase the at least one financialinstrument.
 17. The method of claim 11, wherein the machine learningmodel is iteratively trained to match at least one target buyer with theissuer.
 18. A computer-implemented method for trading in securities, themethod comprising a processor and a memory, the method comprising theoperations of: monitoring, by the processor, current and historicalsecondary market trading levels of correlated securities; receiving, bythe processor, raw data comprising new issue pricing levels andsecondary traded pricing levels from a plurality of dealers and capitalmarkets data sources, wherein the raw data is in a plurality ofdisparate formats; converting, by the processor, the plurality ofdisparate formats into a standardized format; responsive to saidmonitoring, by the processor, predicting in real-time at least one ofissue likelihood, new issue pricing levels and secondary traded pricinglevels of a plurality of issuers of the securities and the new issuepricing levels and secondary traded pricing levels of the securities;converting, by the processor, the new issue pricing levels and secondarytraded pricing levels to equivalent levels in any one of a plurality offoreign currencies and any one of a plurality of interest rates.
 19. Themethod of claim 18, comprising a further step of: processing, by theprocessor, issue transactions following the matching step; andcontinuously monitoring, by the processor, regulatory compliance andreporting mandates associated with the issue transactions and secondarymarket trading transactions.
 20. The method of claim 18, comprising afurther step of: monitoring, by the processor, traditional andnon-traditional buyer preferences, based on historical buying patternsof traditional and non-traditional buyers and aggregating, by theprocessor, buyer preferences from expressions of interest.
 21. Themethod of claim 18, comprising a further step of: generating, by theprocessor, regulatory or market monitoring reports related to deal andnon-deal-related activities in the markets.
 22. The method of claim 18,comprising a further step of: determining, by the processor, currentsecondary market liquidity in real-time, and using aggregate dataderived from public data and proprietary private user data, and based ona set of securities and a group of dealers.
 23. The method of claim 18,comprising a further step of: displaying, by the processor, at least oneof historical trend analysis charts, historical deal analysis charts,and sector and peer comparison charts based on the aggregate data. 24.The method of claim 18, comprising the further steps of: publishing, bythe processor, the pricing levels using the standardized format to aplurality of users; evaluating, by the processor, secondary marketliquidity; comparing, by the processor, covenant terms, and evaluating,by the processor, pricing profiles of the plurality of securities; andwherein the step of predicting the at least one of issue pricing levelsand secondary market security pricing levels of a plurality of issuersof the securities and secondary market pricing levels of the securitiescomprises the further steps of: evaluating, by the processor,participation in primary and secondary capital markets by a plurality ofbuyers based on their established buying pattern analysis andsimultaneously considering attractiveness of the current pricing levels.25. The method of claim 18, comprising further steps of: publishing, bythe processor, pricing indications publicly or privately to other usersamong the plurality of users; collecting and aggregating, by theprocessor, feedback data which is applied in a pricing and buyingpattern analysis process; and enabling, by the processor, the pluralityof users to create a plurality of deals and populate the plurality ofdeals with a plurality of existing reverse inquiries, and gauginginterest in the plurality of deals.
 26. The method of claim 18,comprising a further step of predicting, by the processor, at least oneof an additional likely traditional and non-traditional buyer based onan established buying pattern including ranking based on their size,frequency and recency of purchases.
 27. The method of claim 18,comprising a further step of matching, by the processor, at least onetarget buyer to specific issuers based on the at least one of thepredicted issue likelihood and the predicted new issue pricing levelsand the predicted secondary traded pricing levels.
 28. A computerreadable medium storing instructions executable by a processor to carryout the operations comprising: aggregating raw data from a plurality ofdata sources comprising contemporaneous trading data and fundamentaldata covering a series of time periods and one or more aspects ofquantitative investing and market monitoring, said raw data in aplurality of disparate formats; transforming said raw data in theplurality of disparate formats into a single standard format to generatestructured data; extracting at least one data element of valueassociated with at least one financial instrument from the structureddata in accordance with one or more pre-programmed functions;establishing a plurality of input nodes and an output node for arecurrent neural network model for each aspect of quantitative investingand market monitoring; using the recurrent neural network model to buildat least one model; inputting the structured data into the recurrentneural network model using the plurality of input nodes; training eachof the recurrent neural network using said inputs until an errorfunction associated with an output value that corresponds to an aspectof quantitative investing and market monitoring is minimized; and usingone or more weights from the trained recurrent neural network models toidentify a set structured data by element of value and output that willbe used as an element of value summary for use as an input to each ofone or more predictive models; normalizing each of the one or more setsof structured data by data element of value, refining the sets ofstructured data by the data element of value, creating a summary of arefined transaction data set for each data element of value, and usingthe data element of value summaries as inputs to a predictive model foreach of the one or more aspects of quantitative investing and marketmonitoring where the aspects of quantitative investing and marketmonitoring comprising managing trading activities, managing risk, makingportfolio funding allocations, predicting a time horizon for issuance ofthe at least one financial instrument; predicting an issuer of the atleast one financial instrument within the predicted time horizon;predicting a price of the at least one financial instrument, matching abuyer with the at least one financial instrument, and combinationsthereof, and wherein the predictive models are useful for completingtasks comprising managing trading activities, managing risk, makingportfolio funding allocations, predicting a time horizon for issuance ofthe at least one financial instrument; predicting an issuer of the atleast one financial instrument within the predicted time horizon;predicting a price of the at least one financial instrument, matching abuyer with the at least one financial instrument, and combinationsthereof.
 29. The computer readable medium of claim 28, wherein therecurrent neural network comprises a bidirectional long short-termmemory (BLSTM) neural network architecture.
 30. The computer readablemedium of claim 28, comprising the further operations of: displaying, bythe processor, on the user interface a plurality of user-selectablecriteria comprising time thresholds pertaining to at least one ofhistorical data, contemporaneous data, a sector, a tenor, a bond rating,a model preference, a confidence level and a liquidity score; displayingon the user interface the plurality of user-selectable criteria withselection indicators received by the processor to execute theinstructions to forecast the pricing of the financial instrument. 31.The computer readable medium of claim 28, wherein the at least onefinancial instrument is a bond.